Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Abstract: The present disclosure is directed toward systems and methods for optimizing view-through conversion rates. For example, systems and methods described herein train and utilize a machine learning model that predicts whether providing a digital impression to a particular networking system user will result in a conversion. Systems and methods described herein identify view-through conversions by generating a vector associated with the provision of a digital impression to a networking system user and receiving third-party conversion information during an attribution window. The systems and methods described herein then utilize the vector and conversion information to train the machine learning model to predict future conversions.

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Abstract: An online system receives content items, for example, from content providers and sends the content items to users. The online system uses machine-learning models for predicting whether a user is likely to interact with a content item. The online system uses stored user interactions to measure the model performance to determine whether the model can be used online. The online system determines a baseline model using stored user interactions. The online system determines whether the machine-learning model performs better than the baseline model or worse for each content provider. The online system determines whether to approve the model for online use based on an aggregate normalized performance metric, for example, a metric representing the fraction of content providers for which the model performs better than the baseline. If the online system determines to reject the model, the online system retrains the model.

Abstract: An online advertising system evaluates advertising opportunities for online advertising publishers. The online advertising system tracks online users via various tracking methods to receive advertising data and user information for the online users. The online advertising system identifies and segments the online users based on segmenting criteria that are associated with some interest topics (e.g., demographical information). The system calculates projected advertising revenue for each audience segment and generates an inventory optimization dashboard based on the calculated revenue. The inventory optimization dashboard helps the advertising publishers better understand the online advertising traffic and better optimize their advertising inventory. For example, the advertising publishers may advertise to specific audience segments which tend to purchase the advertised products or services.

Abstract: An online system receives information describing a target group of online system users from a third party system that includes one or more user properties, which may identify actions to be performed by an online system user for inclusion in the target group. Additionally, information describing the target group includes metadata associated with the user properties identifying access to the user properties by additional third party systems. If an additional third party system requests access to the target group or to the user properties describing the target group, the online system determines whether the additional third party system is authorized to access the target group or the user properties based on the metadata. Further, the online system determines an amount of compensation the third party system is to receive if the additional third party system is authorized to access the target group or the user properties based on the metadata.

Abstract: A social networking system receives an advertisement request including multiple sets of targeting criteria. To increase the number of users eligible to be presented with the advertisement request, the social networking system generates a cluster group associated with each set of targeting criteria. A cluster group associated with a set of targeting criteria includes users satisfying the targeting criteria and additional users that do not satisfy the targeting criteria. The social networking system determines an amount of overlap between the cluster groups. If the amount of overlap equals or exceeds a threshold value, the social networking system combines the cluster groups to generate an overall group associated with the advertisement request.